Questions tagged [datasets]

For questions related to sets of data and their use in AI.

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Master theorem about polynomial classifiers?

Does anyone know if there is a theorem or counterexample establishing whether or not for any given binary classification task in some finite (possibly large) dimensional vector space of attributes, ...
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How to identify pulse pattern in data

So I am using a pulse sensor which is giving out a certain data. When I place my finger on the sensor and see the data plotted in a graph it shows a varying (non-repetitive as no two pulses are ...
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Meaning of Large Dataset for machine learning

Some online answers about parameters in machine learning mention that it is dependent on the size of dataset we have (if it is a large dataset or not). Is this size related to the number of samples we ...
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1 answer
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combine two features in dataset?

I have a data set containing the number of security gaps and the level of that gap for a specific website. Now suppose I have 2 features in this data set, the first feature is the number of a ...
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1 vote
1 answer
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Datasets input at model.fit produce unexpected results of training loss vs validation loss

Im trying to train a neural network (VAE) using tensorflow and Im getting different results based on the type of input in the model.fit. When I input arrays I get normal difference between the ...
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1 answer
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Should I include overlapping (input) Data in my training data

If I have time dependent data and want to predict the relative change for a future time. Should I separate the data so that the input times don't overlap? With an example: I have hourly temperature ...
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1 answer
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What should be taken as random variables in the distributions of datasets?

Consider the following two paragraphs taken from the paper titles Generative Adversarial Nets by Ian J. Goodfellow et.al #1: Abstract We propose a new framework for estimating generative models via ...
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1 answer
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What makes a 'good' dataset

for the usage of ML technologies, having a appropriate dataset is arguably the first and fundamental step one has to tackle by either aquiring a dataset from external sources or creating their own. ...
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1 answer
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Is there a way to improve the low-quality data?

I'm on a robotics team and we've been tasked to write a program to differentiate between a live and dead fish. We've been given ~15 minutes of training footage and it's absolutely terrible. It's low ...
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Is there any framework for generation of synthetic data where we can set how hard data is to classify?

I am comparing two different training methods of deep neural networks and I was wondering are there any frameworks that can generate x, y datasets but with some input parameter or parameters that can ...
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7 votes
2 answers
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Is there an argument against using the (reviewed) predictions of a model as ground truth to further train exactly this model?

I plan to use my predictions as ground truth to continue training my model. These predictions are of course reviewed during this process. Is there an argument against that (reinforcement of slight ...
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Which existing model could be used for wind speed and direction prediction?

I am trying to predict the wind speed and wind direction in a graph network for a geographical area. The dataset includes the start and end nodes, the distance between them, and wind speed and ...
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3 votes
1 answer
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What is the total number of actions and rewards count

Reading this two articles about Reinforcement Learning: Deep Reinforcement Learning with Double Q-learning by Hado van Hasselt et al. Human-level control through deep reinforcement learning by ...
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How to use oxford5k for training?

Generally, we have training data with landmark IDs, their GTs (positive samples), and then separate query images and corresponding positive samples for evaluation. In the Oxford5k or ROxford5k, one ...
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3 answers
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How to deal with an unbalanced dataset?

I'm constructing a feed forward neural network that predicts whether a patient will get a stroke or not. However, my dataset is very unbalanced. Out of 5111 rows, 250 contain patients that have had a ...
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1 answer
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How to create a dataset for binary classification

I would like to classify whether a pot of water is boiling or not using a CNN. Is it enough to take pictures of boiling water using only one pot, or should I use different pots for this to generalize ...
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1 answer
36 views

Having the negative cases in the same batch vs. shuffling the dataset

I am working on a model for an NLP task. The model encodes the text and has a regression output layer. In this task, from each instance (positive), I create several negative cases using a specific ...
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1 answer
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Is there a standard term for the following flaw in the data?

I wonder if following characteristic of data has some standard "professional" or scientific term associated with it. Let's assume that I have a set of dog/cat images labeled 0 for a cat and ...
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What are the "per image" annotations that are generally used for image datasets in AI?

Computer vision is highly benefited by AI algorithms. Image data is abundantly available. There are different varieties of tasks such as image classification, prediction, segmentation, generation, ...
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What is all necessary types of data for a bidirectional RNN to learn embeddings?

Bidirectional RNNs are used for generating the semantic vectors of the text at the sentence level and word level. In order to train a CNN for the classification tasks, images, and labels/outputs are ...
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Predicting single floats based on set of 2 feature arrays each of 100 values

I am trying to predict audio to video desynchronization based on ser od two arrays of lenght 100 which consist of coresponding audio and video samples. The problem is that my labels are single floats (...
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1 vote
0 answers
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What are the types of data in which the order of instances does matter?

In general, the order of instances in the datasets that are used in machine learning is immaterial. But there are exceptions. Timeseries data is one such exception I know. Consider the following two ...
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What to predict in a limited transaction dataset?

I have been given a task with a real transaction dataset. The task is to predict something using either logistic regression or simple binary classification. The columns are as follow: Transaction ID ...
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2 votes
0 answers
47 views

Does the Frechet Inception Distance (FID) consider color?

I was wondering if the Frechet inception distance for two colored datasets would be the same than the FID calculated for the same datasets converted to grayscale. I know that it depends on the feature ...
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1 vote
1 answer
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Is there any simple example for volumetric data except from physics and medicine?

Recently I heard about the term volumetric data. The definition for volumetric data is as follows #1: Definition Volumetric data is typically a set S of samples $(x, y, z, v)$, representing the value ...
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2 votes
0 answers
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Do any practical deep learning algorithms deal with tensors containing non-real entries?

In deep learning, most of the applications are from text and images. Both text and images can be converted into a tensor of real numbers. Other than both mentioned above, there may be some other real-...
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1 answer
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Generating a dataset from data with "assumed" lables

I've got a task similar to the following: Out of x amount of people, I need to predict, who could be a good athlete and who not. The thing is, I don't have data on the athletic performance of those ...
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1 vote
1 answer
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Can I use the transformers for the prediction of historical data?

Can I use the transformers for the prediction of wind power with the historical data? Dataset Datetime, Ambient temperature (Degree), Dewpoint (Degree), Relative Humidity\n (%), Air Pressure, Wind ...
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What are the pros and cons of using a normal positional encoding in an adjacency matrix?

I understand that a normal positional encoding helps a transformer to understand pictures better and that it allows the (otherwise permutational invariant transformer-network) to create relationships ...
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3 votes
1 answer
142 views

How to determine the quality of synthetic data?

I'm working on a VAE model to produce synthetic data of X-Ray diffraction spectrums. I try to figure out how I can measure the quality of the spectrums. The goal would be to produce synthetic data ...
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1 vote
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Recommended way to spilt image sequence for training/validation/testing

For object detection tasks I have a few minutes of video footage from a surveillance camera, converted to a sequence of images and ground truth bounding boxes for all people walking by. Now what's the ...
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2 votes
1 answer
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How do you handle unbalanced image datasets?

I have an image data set on which I am training a CNN. The data set is slightly unbalanced. So, my solution up till now was to delete some images of the majority class. But I now realize that there ...
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8 votes
3 answers
882 views

Is it okay to use publicly available Instagram videos to train an AI?

Since I haven't found any good training data for my university project, I want to use pictures and videos from public Instagram profiles. Am I allowed to do that?
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What are the different possible usages of the word "i.i.d" in machine learning?

The acronym "iid" stands for "independent and identically distributed". It is a property of a sequence of random variables. You can read here for more details. This question is ...
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1 vote
1 answer
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Which of the following probability distribution is generating an iid dataset?

Let $X_1, X_2$ be two discrete random variables. Each random variable takes two values: $1, 2$ The probability distribution $p_1$ over $X_1, X_2$ is given by $$p_1(X_1=1, X_2 = 1) = \dfrac{1}{4}$$ $$...
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1 vote
2 answers
133 views

How can we "draw i.i.d" from any probability distribution?

Consider the following paragraph from 2 Learning in High Dimensions in from of the paper titled Geometric Deep Learning Grids, Groups, Graphs, Geodesics, and Gauges ...
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0 votes
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31 views

Modelling of output neuron for mixed features?

A dataset in artificial intelligence, in general, consists of some features (say $n$). Assume that $m$ among them are output features. I want to model this function using a neural network. So, input ...
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0 votes
1 answer
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Why disentangling the features of variation in representation?

Consider the following excerpt from abstract of the research paper titled Better Mixing via Deep Representations by Yoshua Bengio et al. It has been hypothesized, ...
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What are mathematically the factors of variation in deep learning?

The following paragraph from an answer tells us about factors of variation Factors of variation are some factors which determine varieties in observed data. If that factors change, the behaviour of ...
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0 votes
1 answer
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How to handle an unbalanced dataset when training object detection algorithms?

I am training an object detection model, and I have some very highly unbalanced data annotations. I have almost 11,000 images, all with dimensions of 1024 $\times$ 1024. Within those images I have the ...
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What's the best way to feed stories to a neural network?

I'm trying to train a model that would generate stories. I have a dataset of 2000 stories prepared. They are tokenized and one-hot encoded. I can't load them all at once as a one big dataset, because ...
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0 votes
2 answers
60 views

Why data required for hyperparameter tuning is considered as an additional data?

Any parametric model may have parameters as well as hyperparameters. Learning algorithm deals with parameters and hyperparameters should be dealt outside learning algorithm. Consider the following ...
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1 vote
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Is it true that real world data is highly discontinuous?

A function $f$ is said to be continuous at a point $c$ if it satisfies three properties: Should be defined at the point $c$ Left and right-hand limits at $c$ must be equal i.e., the limit must exist ...
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0 votes
0 answers
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Adding data to training results in loss random peaks

I have succesfully trained ssd_mobilenet_v2_keras for object detection, with a dataset of about 3700 images. Now I have more images to add. I tried adding only a few images (150-300) to see what ...
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Which Neural Network Topology to choose, are Transformers suitable?

I have a regression problem and I am not quite sure which architecture to choose. I never worked with transformers before, but I generally understand how they work and I think they might be suitable. ...
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0 votes
1 answer
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Data analysis before feeding to ML pipeline

I'm new to machine learning and I've been working through a dataset of ~3000 records with ~100 features. I've been hand rolling Python and R scripts to analyse the data. For example, plotting the ...
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1 vote
1 answer
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Is there a way to parallelise the RL training on multiple stocks to avoid the memory issue?

I have some plans in working with Reinforcement Learning in order to predict the stock price movement. For a stock like TSLA some training features might be the pivot price values and the set of the ...
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1 vote
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Training on the dataset in parts vs training on the whole dataset

What is the difference between these two situations? are they the same ? #1 : train a model 20 epochs on the whole dataset #2 : divide dataset into n-parts then train the model 20 epochs on each part ...
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0 votes
1 answer
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An online editor that allows data labeling format [closed]

I have a set of students (~20) that will work on annotating data for an NLP project. The annotation task will be as in the following: ...
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1 vote
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Batch normalization for multiple datasets?

I am working on a task of generating synthetic data to help the training of my model. This means that the training is performed on synthetic + real data, and tested on real data. I was told that batch ...
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